1. Field of the Invention
The present invention relates generally to risk assessment, and more specifically to the assessment of risk associated with events of nature.
2. Discussion of the Related Art
Tropical cyclones, also referred to as hurricanes, are among the most dangerous and costly natural disaster affecting mankind. The Galveston hurricane of 1900 was the deadliest natural catastrophe in U.S. history, and in 1998, hurricane Mitch killed over 11,000 people in central America. Although loss of life in more developed countries has been greatly reduced by successful warning programs and evacuation strategies, property losses are rapidly increasing due to continuing construction and economic growth in hurricane-prone areas, such as the coast of the Gulf of Mexico and southern Florida. For example, hurricane Andrew which struck the U.S. in 1992 caused over $32 billion of property damage and recent hurricanes Ivan and Katrina have caused substantial loss of life along with billions of dollars worth of damage.
Much of the property damage and loss of life associated with hurricanes results from high winds speeds and wind-induced storm surges, such as the storm surge associated with hurricane Katrina that caused severe flooding of New Orleans. Consequently, there have been several efforts to monitor and track storms, and to assess risks associated with storm winds.
Current techniques for estimating the probability that a hurricane will strike a given location are based on historical compilations of hurricane tracks and intensities. An example of such historical compilations includes the so-called “best track” data compilations maintained by forecasting operations such as the National Oceanic and Atmospheric Administration's Tropical Prediction Center (TPC) and the U.S. Navy's Joint Typhoon Warning Center (JTWC). These records generally contain information about the position (e.g., latitude and longitude) of the storm center measured every six hours, together with a single intensity estimate (e.g., maximum wind speed or central pressure) for each six hour time period. For example, such data can be used to assess regional risk by dividing a large area into a grid and assigning each cell a “risk factor” based on the frequency of event in that cell as derived from the collected historical data. Such methods are described, for example, in U.S. Pat. No. 5,839,113 to Federau et al. and in International Publications WO 2005/088556 and WO 2005/088496 to Risk Management Solutions, Inc.
One example of a conventional technique for assessing risk of wind speed at a given location is described in a paper by Georgiou et al (Georgiou, P. N., A. G. Davenport and P. J. Vickery, 1983: Design wind speeds in regions dominated by tropical cyclones. J. Wind Eng. Ind. Aerodyn., 13, 139-152). Georgiou's technique involves fitting standard distribution functions, such as log-normal and/or Weibull distributions, to the distribution of maximum intensities of all historical storms coming within a specified radius of the point of interest. Then, drawing randomly from such distributions, Georgiou uses standard models of the radial structure of storms, together with recorded translation speed and landfall information, to estimate the maximum wind likely to be achieved at the point of interest.
Other examples of conventional risk assessment techniques include those described in papers by Chu and Wang (Chu, P. S. and J. Wang, 1998: Modeling return periods of tropical cyclone intensities in the vicinity of Hawaii. J. Appl. Meteor., 37, 951-960) and Darling (Darling, R. W. R., 1991: Estimating probabilities of hurricane wind speeds using a large-scale empirical model. J. Climate, 4, 1035-1056). These techniques use empirical global distributions of relative intensity (which is the ratio of actual to potential intensity, the latter being a measure of the thermodynamic potential for hurricanes) together with climatology of potential intensity to infer local intensity distributions. Potential intensity is defined as the maximum wind speed theoretically attainable in tropical cyclones given large-scale thermodynamic conditions and is easily calculable from large-scale observed or modeled atmospheric fields. A similar approach is described in a paper by Murnane et al. in which global estimates of actual (rather than relative) hurricane wind intensity cumulative probability distributions are used to infer local intensities (Murnane, R. J., C. Barton, E. Collins, J. Donnelly, J. B. Elsner, K. Emanuel, I. Ginis, S. Howard, C. W. Landsea, K. B. Liu, M. Malmquist, M. McKay, A. Michaels, N. B. Nelson, J. O'Brien, D. Scott and T. Webb, 2000: Model estimates of hurricane wind speed probabilities. Eos, 81, 433-438).
Another approach is described in a paper by Vickery et. al (Vickery, P. J., P. F. Skerjl and L. A. Twisdale, 2000: Simulation of hurricane risk in the U.S. using empirical track model. J. Struct. Eng., 126, 1222-1237). Vickery uses statistical properties of both historical tracks and historical intensities to generate a large number of synthetic storms in the North Atlantic basin. Vickery then models six hour changes in direction, translation speed and intensity along each track as linear functions of previous values of those quantities as well as of position and sea surface temperature. Thus, Vickery generates a large database of synthetic storm tracks using previous track history and local climatology, and couples to these tracks historical intensity data.